It has no pooling layers and is therefore fully connected in the CNN sense. Autoencoding CNNs enjoy several properties that are desirable for the task at hand: As opposed to recurrent layers, the calculations of the convolutional layers are completely parallelisable, increasing computational speed. Furthermore, the fact that the the network is many-to-many frame, means the network is better adapted at learning contextual information. The symmetric structure of the network allows for a straightforward use of skip connections, which has already been proved successful in raw audio-to-raw audio speech enhancement [1]} as well as image denoising [2]}. These skip connections increase the expressive power of the network by letting information bypass the bottleneck and ease the flow of gradients, thereby improving the learning capacity of the network.
